The present disclosure relates generally to assessment of dynamic cerebral autoregulation, specifically, the assessment of dynamic cerebral autoregulation function by analyzing multiple physiological signals.
Cerebral autoregulatory mechanisms are engaged in compensating for metabolic demands and perfusion pressure variations under physiologic and pathologic conditions.
Cerebral microvasculature controls perfusion using myogenic and neurogenic regulation by adjusting small-vessel resistances in response to beat-to-beat blood pressure (BP) fluctuations. Dynamic cerebral autoregulation (CA) reflects the ability of the cerebral microvasculature to control perfusion. Some conventional CA assessment techniques (such as transfer function technique) normally simulate cerebral regulation by linear and time-invariant mathematical models, which treat blood pressure variation as input and cerebral blood flow as output. The relationship between BP and cerebral blood flow velocity (BFV) is explored by a transfer function. The gain and phase shifts between power spectra of BP and BFV are calculated. Alterations in BP-BFV relationship under pathologic conditions can be identified by the transfer function.
A major drawback of some conventional CA assessment techniques is that the analysis is based on Fourier transform that is limited to superimposed sinusoidal signals with constant amplitudes and periods. Conventional CA assessment techniques often cannot accurately analyze non-stationary BP and BFV signals used in clinical diagnosis. Providing accurate, reliable, and noninvasive assessment of dynamic cerebral autoregulation continues to be a challenge in medical diagnostics.